No need to add the names of the properties as an attribute as they are note used out of the constructor.

Rather than fitting again the best model to retrieve the proper scaling, let's keep it. It takes little memory.

Store the Bayesian estimates in dictionaries of SharedArray rather than in one large SharedArray. This has the advantage to allocate smaller arrays, which is generally easier. Using dictionaries also makes it easier to access the values of a given property. First patch in a series to use dictionaries more extensively.

Make the data member of SharedArray more robust by adding a setter. This setter checks that the variable to be assigned is indeed a ctypes.Array. This prevents from accidentally erasing data with a Numpy array for instance.

rename varibles in  _check_filters (observations.py) to reflect that  properties are also included, fixes documentation

Modify ObservationsManagerPassbands to iterate over an instance of observationclass,  modify Workers accordingly and to include the properties and modify pdf_anaysis/utils to include properties in compute_chi2, _cimpute_scaling and dchi2_over_ds2

Include properties in _check_filters, _check_errors, _check_invalid (only extensive) and add_model_error functions in observations.py. rename errors as bands_errors

Add ParameterManager as input to ObserbationsManagerPassbands class, this is use to separate the intensive properties to fit from the extensive and add them to the class. Modify pdf_analysis/__init__.py to call ParameterManager befor ObservationsManager

modify models.py to use get_info from utils, add intprops and extprops property to ModelsManager class

For more reliability we now save the chi² by blocks. Otherwise it could lead to some crashes when there was more than 1 redshift.

Ensure that we do not try to fit bands that have an error bar of 0 mJy. We now simply discard such bands.

The standardisation of error bars must be done on the final estimates, not on the estimates for a single block.

Use a much more readable formula to compute the uncertainties. We use the fact that Var(X) = E(Var(X)) + Var(E(X)). At the same time, broadcast the weights array to the same shape as the other arrays in order to simplify the syntax for the computation.

Read the list of sed modules from pcigale.ini. This is more flexible than inferring this list from the input parameters file as some modules can be missing (for instance when using the default values for all the parameters of a given module).

Add a results manager to store the results of the analysis (bayesian and best fit estimates, best chi², etc.). Several such managers can be merged to combine appropriately the analysis of the observations in different blocks. This is the base class. The implementation will come in subsequent patches.

More correct block size computation to ensure that all the models are computed and that each block contains all the redshifts for a given model.